Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

The data collected from taxi vehicles using the global positioning system(GPS)traces provides abundant temporal-spatial information,as well as information on the activity of drivers.Using taxi vehicles as mobile sensors in road networks to collect traffic information is an important emerging approach in efforts to relieve congestion.In this paper,we present a hybrid model for estimating driving paths using a density-based spatial clustering of applications with noise(DBSCAN)algorithm and a Gaussian mixture model(GMM).The first step in our approach is to extract the locations from pick-up and drop-off records(PDR)in taxi GPS equipment.Second,the locations are classified into different clusters using DBSCAN.Two parameters(density threshold and radius)are optimized using real trace data recorded from 1100 drivers.A GMM is also utilized to estimate a significant number of locations;the parameters of the GMM are optimized using an expectation-maximum(EM)likelihood algorithm.Finally,applications are used to test the effectiveness of the proposed model.In these applications,locations distributed in two regions(a residential district and a railway station)are clustered and estimated automatically.

Characterizing the urban spatial structure using taxi trip big data and implications for urban planning

Urban spatial structure is an important feature for assessing the effects of urban planning.Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments.Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures.However,these methods cannot efficiently reflect the influence of human activities.With the wide application of big data,analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning.In this study,we constructed a human-activity space network using the taxi trip big data.Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning.This method was applied to a case study based on one-month taxi trip data of Dongguan City.Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020,which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan.We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure.The analysis demonstrated that the taxi trip data are important big data on social spatial perception,and taxi data should be used for evaluating spatial structures in future urban planning.

APFD:an effective approach to taxi route recommendation with mobile trajectory big data

With the rapid development of data-driven intelligent transportation systems,an efficient route recommendation method for taxis has become a hot topic in smart cities.We present an effective taxi route recommendation approach(called APFD)based on the artificial potential field(APF)method and Dijkstra method with mobile trajectory big data.Specifically,to improve the efficiency of route recommendation,we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates.Then,based on the APF method,we put forward an effective approach for removing redundant nodes.Finally,we employ the Dijkstra method to determine the optimal route recommendation.In particular,the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing.On the map,we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony(AC)algorithm,greedy algorithm(A*),APF,rapid-exploration random tree(RRT),non-dominated sorting genetic algorithm-II(NSGA-II),particle swarm optimization(PSO),and Dijkstra for the shortest route recommendation.Compared with AC,A*,APF,RRT,NSGA-II,and PSO,concerning shortest route planning,APFD improves route planning capability by 1.45%–39.56%,4.64%–54.75%,8.59%–37.25%,5.06%–45.34%,0.94%–20.40%,and 2.43%–38.31%,respectively.Compared with Dijkstra,the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency.In addition,in the real-world road network,on the Fourth Ring Road in Beijing,the ability of APFD to recommend the shortest route is better than those of AC,A*,APF,RRT,NSGA-II,and PSO,and the execution efficiency of APFD is higher than that of the Dijkstra method.